International Journal of Wireless & Mobile Networks (IJWMN...

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 11, No. 6, December 2019

DOI: 10.5121/ijwmn.2019.11603 35

IMPROVED PROPAGATION MODELS FOR LTE PATH

LOSS PREDICTION IN URBAN & SUBURBAN

GHANA

James D. Gadze, Kwame A. Agyekum, Stephen J. Nuagah and E.A. Affum

Department of Telecommunication Engineering,

Kwame Nkrumah University of Science and Technology, Ghana

ABSTRACT

To maximize the benefits of LTE cellular networks, careful and proper planning is needed. This requires

the use of accurate propagation models to quantify the path loss required for base station deployment.

Deployed LTE networks in Ghana can barely meet the desired 100Mbps throughput leading to customer

dissatisfaction. Network operators rely on transmission planning tools designed for generalized

environments that come with already embedded propagation models suited to other environments. A

challenge therefore to Ghanaian transmission Network planners will be choosing an accurate and precise

propagation model that best suits the Ghanaian environment. Given this, extensive LTE path loss

measurements at 800MHz and 2600MHz were taken in selected urban and suburban environments in

Ghana and compared with 6 commonly used propagation models. Improved versions of the Ericson, SUI,

and ECC-33 developed in this study predict more precisely the path loss in Ghanaian environments

compared with commonly used propagation models.

KEYWORDS Propagation Models, Path loss Exponent, Root Mean Square Error, Signal Reference Received

Power(RSRP).

1. INTRODUCTION

Cisco's visual networking index, 2017-2022 predicts that the IP traffic recorded annually around

the globe is estimated at 4.8 Zb by 2022. This translates to a threefold increase over the next five

years [1]. Mobile data subscription in Ghana as of July 2018 stood at twenty-nine million, one

hundred and eighty-one thousand, eight hundred and sixty-three (29,181,863) [2]. For a country

with an estimated total population of thirty million [3], it shows the high demand for data and

broadband services.

With this growing demand for bandwidth in mobile communication as user numbers keep

increasing significantly, mobile networks have evolved from 1G - 4G to meet the demand over

the years. In Ghana, Blu telecom, Busy internet, Surfline, MTN, and recently Vodafone have

commercially deployed 4G LTE networks for higher throughputs and improved user experience.

However, this hasn't been completely achieved since the expected throughput of 100Mbps is

barely realized leading to dissatisfaction among customers. This has resulted in a lot of complaints

and sanctioning from the National communications authority [4].

Transmitted signals from a base station suffer severe attenuation as they propagate through space

leading to degradation in signal strength and quality [5]. This severe attenuation is introduced due

to reflection, diffraction, and scattering of the signal as it impinges on obstacles. For subscribers

of a network who have varying mobility, it is imperative to design a mobile network so that they


36

have robust signal levels at all locations. To achieve this, Conditions for radio propagation need

to be well understood and predicted as accurately as possible. propagation models are

instrumental in wireless network planning as they support interference estimates, frequency

assignments, and cell coverage assessment and other parameters[6].

Empirical propagation models that are mostly used are however environment specific and are

developed based on a specific propagation environment of interest [7]. Any little deviation in

characterizing the propagation environment under investigation affects the efficiency of

propagation models designed from the area[8],[9]. Therefore, the use of propagation models in

settings other than those intended to be used might lead to inaccurate prediction which affects

system performance[10],[11]. To investigate these claims, the approach adopted in this project is

to take Signal Reference Received Power (RSRP) values from deployed cell sites and compare

with predictions from 4 propagation models at 800MHz and 6 models at 2600MHz. This approach

will help us develop modified and improved versions of already existing propagation models

suited for the Ghanaian environment.

The rest of the paper is organized as follows: Section 2 reviews some relevant work in this field.

Section 3 presents the measurement procedure at investigated environments and empirical

propagation models under consideration. Results are presented in Section 4 and Section 5

concludes the study.

2. RELATED WORKS

Considering the increased demand placed on mobile communication, higher throughputs and

seamless connectivity, designing LTE networks in compliance with the performance metrics it

promises is crucial. Numerous studies have gone into finding propagation models that predict

accurately the path loss in the USA, Europe, Africa, and Asia to improve network performance

for both voice and data communication.

How best current propagation models will perform when used in wireless environments other

than those originally intended for frequently deviate from the ideal [6]. Numerous studies around

the globe, however, show that many industry-standard path loss models perform effectively when

adjusted to measured data from these areas [12].In [12], Path loss measured data at 3.5GHz in

Cambridge was compared with the predictions of three empirical propagation models. Results

indicated that the SUI and COST-231 models over-estimated path loss in this environment. The

closest fit to the measurement data was the ECC-33 model. It was therefore recommended for use

in urban environments.

The least-square method was used in [13] to optimize the Hata empirical path loss model for

accurate prediction suited to a suburban area in Malaysia. Outdoor measurements were taken in

Cyberjaya, Malaysia at a frequency range of 400MHz to 1800 MHz. Measurements were then

compared with the existing models from which the Hata model showed the best fit. The optimized

Hata model was used and validated in the Putrajaya region to detect the relative error to evaluate

its efficiency. Smaller mean relative error was recorded hence showing that the optimization was

done successfully.

Propagation models are presented in [14] for LTE Advanced Networks. Path loss for varying

environments ( rural, suburban and dense urban)were computed using the following propagation

models, COST-231 Walfisch–Ikegami model, SUI, ECC-33, Okumura extended Model and

COST-231 Hata Model using MATLAB. Three frequencies between 2.3GHz and 3.5 GHz were

considered in this work. Results presented indicated the COST-231 Hata model agreed better,

giving the least path loss in all the environments compared with the other models. This work,


37

however, did not compare the prediction of empirical models with measured data but only based

on the model with the least path loss. The conclusion made favoring the cost 231 Hata model by

simulation as agreeing best in all environments might be misleading.

Extensive measurements in [10] taken in Lagos at a frequency of 3.4GHz made a comparison

with 6 standard propagation models. It was concluded that the COST 231-Hata and Ericson

models showed the best performance in urban and suburban areas. Recent works in [16] also

compared the efficiencies of empirical, heuristic, and geospatial methods used for signal path loss

predictions using data collected in urban Nigerian cities to develop path loss models. The

developed models and empirical models were compared with field measured data. All models

gave acceptable RMSE values excluding the ECC-33 and Egli models. Empirical models were

the simplest and most commonly applied of the three techniques submitted. Their work, therefore,

emphasized the further improvement of empirical models for optimum prediction. A hybrid of

heuristic and empirical models for prediction was recommended to decrease the errors associated

with empirical models.

Works have also gone into comparing path loss of urban and suburban areas and to ascertain if a

particular propagation model can be used for both settings.[17] showed that propagation models

in urban areas experience higher losses compared with suburban areas. For all environments, no

single model could be proposed.

On the background that deployed WiMAX networks, failed to meet the optimum service quality

requirements for delivering continuous wireless connectivity requests in the sub-Sahara region

needed for emerging mobile applications, [18] investigated the throughput performance of a

deployed 4G LTE Site to ascertain if LTE meets the bandwidth demand needed for data-centric

broadband applications. Field data from a deployed 4G LTE BS in Ghana operating at 2600 MHz

recorded a maximum throughput of 29.9 Mbps per sector. A maximum throughput of 62.318

Mbps was recorded at the downlink for customers within 2.5 km of the cell range from the BS. It

was concluded that 4G LTE can meet the ever-increasing demand of Ghanaians for broadband.

This conclusion was made after comparing these throughputs with the desired throughput required

to sustain datacentric broadband applications.

Works in the Ghanaian environment focusing on WiMAX networks in the 2500-2530 MHz band

was presented in [11]. The measurement from a deployed WiMAX site around the university of

Ghana, Accra was compared with the prediction of four empirical models. The extended COST-

231 model was selected as the model that best fits the measured data because it recorded the least

RMSE and a higher correlation coefficient. This model was recommended therefore for efficient

radio network planning in Ghana and the sub-region at large. It was also concluded that no

particular propagation model can be used to forecast coherent outcomes for all propagation

settings. The reason for this was the variations in weather and geography. Recommendations were

made to consider varying terrain parameters.

Intensive measurements in separate environments must be conducted to parameterize a model.

The parameters of the channel model are then adjusted to suit the measurement outcomes [19]. It

is imperative therefore from the works reviewed to evaluate the performance of industry-standard

propagation models proposed for 4G LTE networks by considering different Ghanaian

environments. With several path loss models performing differently in different environments, it

is, therefore, essential to determine which of the most frequently used models is best suited for

4G LTE networks in Ghana. Further improving the suited model for more accurate prediction

pertinent to the Ghanaian and Sub-Saharan environment will facilitate effective deployment of

LTE networks by operators, meeting the promises the Standard came with. This, in the end, will


38

afford subscribers the chance to enjoy seamless connectivity leading to customer satisfaction and

loyalty.

3. MEASUREMENTS

3.1 Procedure

Received signal reference power (RSRP) values in dBm were taken at 10 Base stations in seven

selected areas in Ghana with varying environmental conditions. A drive test was conducted using

phones connected via the USB port to a computer with LTE software (Genex probe) installed on

it. Genex probe serves as a data collection software interface. A GPS was attached for location

finding and tracking distance covered. The frequency was set to 800MHz for the first test case at

five base stations and 2600MHz for the second test case for the other five base stations. At the

various sectors of each LTE site in these environments, RSRP values at a varying distance starting

from a reference distance (do) of 50m to 500m with 50m intervals were recorded. The Transmit -

Receiver distance was limited to 500m to reduce the impact of interference from neighboring cells

and also to cater for obstructions in the way of the drive. A receiver antenna height of 1.5m was

maintained throughout the measurement campaign. Measured data is sent via the phones to the

computing device which stores the data as recorded log files. These recorded log files are then

interpreted and analyzed. Field measurements were taken between February and May. The RSRP

in dBm was taken along the LOS and NLOS of the fixed base stations with heights ranging

between 16m and 35m. The laptop having GENEX software installed on it, the phone and the

GPS were set up in the drive test vehicle as shown in figure 1

Figure 1 Measurement set up

3.2 Description of Environments

Drive test measurements were taken in the following environments in Ghana.

1. Adum: This is an urban area located in the central hub of Kumasi, in the Ashanti Region

of Ghana with coordinates 6.6919°N,1.6287°W. It is highly populated and Characterized

by a lot of business activity. Present in Adum are a lot of high-rise buildings.

2. Techiman: This is an urban area that serves as the capital of the newly created Bono East

Region of Ghana with coordinates 7.5909° N, 1.9344° W. It is characterized by quite

many high-rise buildings and a lot of farming and business activities.

3. Agogo: This is a Suburban area in the Asante Akyim North Municipal District of the

Ashanti Region of Ghana with coordinates 6.7991° N, 1.0850° W. Agogo is

approximately 80 kilometers east of Kumasi, with moderate population and buildings.

Buildings are mostly not high rise and are a little isolated from each other. The terrain is

relatively flat.


39

4. Afrancho: It is a Populated suburban Community in the Bosomtwe District of the Ashanti

region of Ghana with coordinates 6° 33' 0" N,1° 38' 0"W. it is characterized by relatively

hilly terrain with the presence of valleys.

5. New Dorma: Suburban area in the Brong Region of Ghana. It is Characterised by a

mixture of flat and hilly terrains covered with a lot of vegetation. It lies on coordinates 7°

16' 39" N,2° 52' 42"W.

6. Berekum: This is a Municipal located in the Bono Region of Ghana. It lies on coordinates

7° 27'N,2° 35'W.

7. Sunyani: This is an Urban populated city serving as the capital of the Bono Region of

Ghana. Sunyani is surrounded by the forested Southern Ashanti uplands. It lies on

coordinates 7° 20'N,2° 20'W.

Modeling Parameters

Parameters used in generating the path loss for the different propagation models are given in Table

1. Table 1 Modeling parameters

3.3 Propagation Models

The following propagation models were considered in this work.

I.Free Space Path Loss Model

II.Hata Model

III.COST- 231Model

IV.ECC-33 Model

V.Stanford University Interim (SUI) Model

VI.Ericson Model

3.3.1 Free Space Path Loss

Path loss estimation by this model is as given in equation (1)

Pl = 32.44 + 20log(d) + 20log(f) (1)

f =frequency in MHz , d =distance in Km

parameters values

Operating frequency 800MHz &2600MHz

Transmit power 46dBm

Transmitter Antenna

Height

Techiman 35m

Adum 24m

Agogo 25m

Afrancho 32m

New Dorma 32m

Berekum 32m

Sunyani 25m

Shadowing factor urban 10.6 dB

suburban 8.2 dB

Distance 50 m – 500 m Reference distance (do) 50m

Receiver antenna height 1.5m


40

3.3.2 HATA Model

Path loss for the Hata model as given in [5] and [21] is given in (2).

Plurban

(dB) = 69.55+ 26.16log( f ) -13.82log(ht) -ah

r

+[44.9 - 6.55log(ht)]log(d)

(2)

in suburban areas path loss is computed as in (3)

Plsuburban

(db) = Plurban

- 2[log(f

28)]2 -5.4 (3)

f =frequency in MHz, d=distance in Km, hr =mobile antenna height in meters and h

t = base

station antenna height in meters

In small and medium cities,

ahr= (1.1log( f ) - 0.7)h

r- (1.56log( f )- 0.8) (4)

For large cities,

for f > 300MHz (5)

(5)

3.3.3 COST-231 Model

The path loss equation for this model expressed in dB as given in [24] is shown below

PL(dB) = 46.3 + 33.9 log(f) -13.82 log(hte) -a(ha) + (44.9-6.55 log(hte)) log(d) + Cm

(6)

where is the frequency specified in , is the distance between the base station and

mobile antennas given in km, hte is the base station antenna height above ground level in

meters.hre is the mobile antenna height in meters, is defined as 0 dB for suburban or open

environments and 3 dB for urban environments.

is defined for large areas as

𝛼(ℎ𝑎) 𝑑𝐵 = 8.29(log (1.54ℎ𝑟𝑒))2 − 1.1 𝑓𝑜𝑟 𝑓 ≤ 300𝑀𝐻𝑧 (7)

α(ha) dB = 3.2(log (11.75hre))2-4.97 for f > 300MHz (8)

In medium or small cities,

(9)

3.3.4 ECC-33 Path Loss Model

The path loss equation for this model is given in [15]

(10)

ahr= 3.2[log(11.75h

r)]2 - 4.97

( )2

8.29 log 1.54 1.1 for f 300MHzr rh h = −

f MHz d

Cm

a(ha)

a(ha)dB = (1.1log( f )- 0.7)h

r- (1.56log( f )- 0.8)

– – fs bm b rPL A A G G= +


41

Where is the free space path loss, is the basic median path loss , is the transmitter

antenna height gain factor and is the receiver antenna height gain factor.

Each of these parameters is expressed fully as;

(11)

(12)

(13)

When considering medium city environments

(14)

For large cities,

(15)

where is frequency expressed in is the distance between the transmitter and receiver

in , is the transmitter antenna height in meters and is the receiver antenna height in

meters.

3.3.5 Stanford University Interim (SUI Model)

Path loss for this model is given in (16) as presented in [22]

PLSUI

= A+10g log(d

do

) + s for f < 2GHz (16)

8.2dB < s <10.6dB

d is the distance between the transmitter and receiver

do=50m f is the frequency in MHz

A= 20log(4pd

o

l) (17)

g = a - bhb+c

hb

(18)

Where;

hb is the base station antenna height 10m< h

b< 80m

l is the wavelength expressed in meters. a, b and c are terrain factors specified in Table 2

fsA bmA bG

rG

10 1092.4 20 ( ) 20 ( )Afs log d log f= + +

2

10 10 1020.41 9.83 ( ) 7.894 ( ) 9.56[ ( )]bmA log d log f log f= + + +

2

10 10( / 200) 13.958 5.8[ ( )]bG log hb log d= +

( ) ( ) 42.57 13.7 10 10 – 0.585rG log f log hr = +

0.759 1.862rG hr= −

f GHz d

Km hb hr


42

g( f ) = 44.49log( f ) - 4.78(log( f ))2a+ b+ c

Table 2 Terrain Parameters

Parameter Category A Category B Category C

a 4.6 4 3.6

b 0.0075 0.0065 0.005

c 12.6 17.1 20

3.3.6 Ericson Model

The equation specifying path loss for this model as presented by J. Milanovic et al [26] is shown

in equation (19).

(19)

(20)

The parameters a0, a1, a2, and a3, given in equation (19) are constants, that can be tuned to best

fit specified propagation conditions. The default values of a0, a1, a2, and a3 for different

environment categories are specified in Table 3

Table 3 Default values of a0, a1, a2 and a3

Category of Area 𝑎0 𝑎1 𝑎2 𝑎3

Urban 36.2 30.2 12.0 0.1

Suburban 43.20 68.93 12.0 0.1

3.4 Path Loss Exponent

The path loss exponent which shows the lossy nature of a particular propagation environment was

computed from the measurement data for each of the areas considered. [23] presents an approach

to finding the path loss exponent as shown in (21)

𝑛 =∑ (𝑃𝑙𝑑𝑜 − 𝑃𝑖) ∗ 10log (

𝑑𝑑𝑜

)𝑘𝑖=1

∑ (𝑘𝑖=1 10log (

𝑑𝑑𝑜

))2 (21)

Where Pi is the received power at the reference distance do, Pldo is the path loss at the reference

distance and 𝑛 is the path loss exponent.

PlEricson

= a0 + a1log(d) + a2log(hb)

+a3log(hb) log(d) - 3.2(log(11.75h

m))2 + g( f )


43

3.5 Root Mean Square Error(RMSE)

The RMSE which measures the difference between the signal power predicted by a model and

the actual measured signal was implemented in MATLAB. It served as a measure of accuracy to

compare forecasting errors of the different propagation models given the drive test measurement

data. It is defined mathematically by equation (22)

(22)

Where represents the measured power value at a specified distance, is the predicted power

value at a specified distance, k represents the number of measured samples.

4. RESULTS

The results presented are two-fold. The first is at an operating frequency of 800MHz and the

second at an operating frequency of 2600MHz.Results are Validated at the end of this section

4.1 Results at 800MHz

The average received power was computed for each of the measurement environments by

averaging the readings taken at the three different sector antennas of the base stations. The mean

received power for the different environments was compared and analyzed by plots against

varying distances from the base station using MATLAB. This is shown in figure 2.

As can be observed from the graph, the Received power decreases as distance away from the Base

station is increased. Deviations from this trend, however, occurred on a few occasions. This was

partly as a result of obstacles and a contribution from the terrain of those environments.

Figure 2 Received power of all Sites

4.1.1 Path Loss of Measured data

The experienced path loss at each measurement location at a distance d(m) was computed as

follows;

Pl(dB) = EIRP(dBm)-Pr(dBm) (23)

2

1

k

i i

k

p p

RMSEk

=

−

=

ipip

50 100 150 200 250 300 350 400 450 500

Distance

-100

-95

-90

-85

-80

-75

-70

-65

-60

-55

Rec

eive

d P

ower

(dB

m)

Techiman

Adum

Agogo

Afrancho

Dorma


44

where 𝑃𝑟 = Mean received power in dBm, EIRP = Effective isotropic radiated power in dBm.

EIRP is given in (24) and (25)

EIRP = Pt + G-L (24)

Where G stands for Gains and L for losses

Typical gains considered are the antenna gains both at the transmitter and receiver end

Typical losses are connector, body and combiner loss

Expanding this yield,

EIRP = Pt + Gt + Gr-Lco-Lcon-Lbo (25)

Pt = Transmit power (dBm),Gt = Gain of Transmit Antenna (dBi),Gr = Gain of Receive antenna

(dBi),Lcon = Connector loss (dB),Lbo = Body loss (dB),Lco = Combiner loss(dB).

The Values of the stated parameters commonly applied in LTE Networks are given by S. A.

Mawjoud [24] as;

Pbts = 40W = 46dBm , Gbts = 18.15dBi, Gms = 0dBi ,Lbo = 3dB,

Lcon = 4.7dB, Lco = 3dB

These parameters are substituted into equation (25)

EIRP = 53.5dBm

The path loss is obtained by substituting the calculated value of EIRP (dBm) and the mean

received power Pr (dBm) into equation (23).

The effect of varying distance on Path loss for each measurement environment was investigated

by plots of path loss versus distance and the graph shown below in figure 3 illustrates this.

Figure 3 Path loss of all environments

It can be observed from the graph given in figure 3 that Path loss increases as the distance from

the Base station increases. Comparing the path loss experienced for all the measurement

environments, the Path loss of Adum and Afrancho is relatively higher compared to the other

areas. The hilly nature of Afrancho and the presence of many high-rise buildings in Adum are

good reasons to support the high path loss in these areas.

50 100 150 200 250 300 350 400 450 500

DISTANCE

110

115

120

125

130

135

140

145

150

155

Pat

h Lo

ss (

dB)

Techiman

Adum

Agogo

D. Afrancho

New Dorma


45

4.1.2 Comparison of Path Loss Measurement Results with Propagation Models

The path loss of each measurement environment was compared with the path loss estimations of

the understudied propagation models at 800MHz for both urban(Adum and Techiman) and

suburban scenarios (Agogo, Afrancho & Dorma).

Figure 4 Path Loss of Adum Compared With Path Loss of Propagation Models

Figure 5 Path loss of Techiman compared with path loss of propagation models

Figure 6 Path loss of Afrancho compared with path loss of propagation models

50 100 150 200 250 300 350 400 450 500

Distance

60

70

80

90

100

110

120

130

140

150

Path

Los

s (d

B)Adum

HATA

SUI

ERICSON

fspl

50 100 150 200 250 300 350 400 450 500

Distance

60

70

80

90

100

110

120

130

140

150

Path

Los

s (d

B)

Techiman

HATA

SUI

ERICSON

fspl

50 100 150 200 250 300 350 400 450 500

Distance

60

80

100

120

140

160

180

200

220

240

Path

Los

s (d

B)

Afrancho

HATA

SUI

ERICSON

fspl


46

Figure 7 Path loss of Agogo compared with path loss of propagation models

Figure 8 Path loss of New Dorma compared with path loss of propagation models

4.1.3 Choice of Propagation Model that best fits Measurement data

Root Mean square error was used as a quantitative measure of accuracy for choosing the

propagation model that best fits the measured data in the Ghanaian environment. The best

propagation model was the model that had the least Root Mean squared errors (Least RMSE).

The RMSE computed for the measurement areas together with the various propagation models

are given in Table 4. Table 4 RMSE Values

Root mean square error(urban)

Environments Hata model SUI model Ericson model

Techiman 30.66 32.96 44.41 17.98

Adum 40.64 40.39 52.31 25.17

Root mean square error(suburban)

Agogo 50.22 45.88 173.99

Afrancho 52.39 48.48 171.18

New Dorma 44.03 39.21 182.86

50 100 150 200 250 300 350 400 450 500

Distance

60

80

100

120

140

160

180

200

220

240

Path

Los

s (d

B)

Agogo

HATA

SUI

ERICSON

fspl

50 100 150 200 250 300 350 400 450 500

Distance

60

80

100

120

140

160

180

200

220

240

Path

Los

s (d

B)

Dorma

HATA

SUI

ERICSON

fspl


47

The Ericson model had the lowest RMSE values in Urban environments as shown in Table 4.This

model is therefore chosen as the model that predicts best in Urban areas in Ghana and it is further

modified and improved for more accurate predictions. In the suburban environments, the SUI

model had the lowest RMSE values and hence was chosen as the best model for path loss

prediction in suburban cities in Ghana. It is also further modified for a more accurate prediction.

4.1.4 Modification of Ericson Model

The Ericson model which best fit measurement in the urban environments was chosen and

modified to fit the measured data in urban environments. To modify and further improve the

Ericson model the mean square error between the urban environments and the Ericson model was

added to the standardized Ericson path loss equation.

a0 + a1* log(d) + a2* log(hb) + a3* log(hb) * log(d) -3.2*(log(11.75*hr))2 + gf + RMSE (26)

RMSE for Adum =17.98

Adding the RMSE yields;

a0 + a1* log(d) + a2* log(hb) + a3* log(hb) * log(d) -3.2*(log(11.75*hr))2 + gf + 17.98 (27)

This new equation with the RMSE added was plotted with measurement data from Adum together

with the initial standardized Ericson model equation and the graph is shown in figure 9. It can be

observed that adding the RMSE to the initial equation improves the accuracy of prediction as the

modified Ericson equation fits best with the measured data.

Figure 9 Comparison of the modified model and original Ericson model for Adum

The values of various parameters a0, a1, a2, a3, hb, hr and gf in the Ericson model suited for an

urban area were substituted into the modified equation and approximated to make the Ericson

equation simple and less tedious to use yet not compromising accuracy. The resulting equation is

as in equation (28)

Plmodified = 68.30 + 30.2log(d) + 0.139log(d) (28)

A similar analysis was carried out for Techiman and figure 10 shows the modified Ericson model

fitting closely to measurement data from Techiman

50 100 150 200 250 300 350 400 450 500

Distance

100

120

140

160

180

200

220

Pa

th L

oss

(dB

)

ADUM

ERICSON ORIG

ERICSON MODIFIED


48

Figure 10 Comparison of the modified model and original Ericson model for Techiman

4.1.5 Modification of SUI Model

Results of RMSE for suburban areas favored the SUI model which had the lowest RMSE values.

On this basis, the SUI model was chosen as the best-fit propagation model for path loss estimation

in suburban areas in Ghana. It was further modified for more accurate predictions in Ghanaian

suburban environments. The RMSE each of Agogo, Afrancho and New Dorma were added to the

original SUI equations and further simplified in equations (30) - (32)

PLSUI = A + 10γlog (d

do) + s ± RMSE (29)

1) modified model for Agogo

Plsui(simple) = 72.68 + 24.54log(d

d0) + RMSE (30)

2)modified model for Afrancho


do) + RMSE (31)

3)modified model for New Dorma


do) + RMSE (32)

A graph comparing the performance of the modified model for Agogo with the original SUI model

is shown in figure 11

Figure 11 Comparison of modified SUI model and original SUI model for Agogo

50 100 150 200 250 300 350 400 450 500

Distance

100

120

140

160

180

200

220

Pat

h Lo

ss (d

B)

Techiman

ERICSON ORIG

ERICSON MODIFIED

50 100 150 200 250 300 350 400 450 500

Distance

70

80

90

100

110

120

130

140

150

Path

Loss

(dB)

AGOGO

SUI ORIG

SUI MOD


49

4.2 Results at 2600MHz

Comparing the path loss predictions of propagation models at 2600MHz with drive test

measurements of five different propagation environments, the ECC-33 model predicted close

to the measurement data. This model was further modified to predict more accurately the path

loss in these environments. This is shown in figures 12 & 13

Figure 12 Comparison of modified ECC-33 model and original ECC-33 model for urban environments

Figure 13 Comparison of modified ECC-33 model and original ECC-33 model for suburban

environments

4.3 VALIDATION

The developed models in this study were validated by calculating the error between the measured

and estimated path loss for the various measurement environments using the modified equations

presented. This is achieved by using equation (28) programmed in MATLAB. The values of

RMSE closer to zero indicate a better fit [25],[11]. Thus, the developed models are described as

valid and suitable for the tested environments since the RMSE between the measured and the

predicted path loss values are closer to zero than the initial RMSE values. Tables 7 & 8 show the

RMSE generated using the developed models in this thesis at 800MHz and 2600MHz

respectively.

50 100 150 200 250 300 350 400 450 500

Distance

105

110

115

120

125

130

135

140

145

150

155P

ath

Lo

ss (

dB

)

Urban

ECC33orig

ECC33modified

50 100 150 200 250 300 350 400 450 500

Distance

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120

125

130

135

140

145

Pat

h L

oss

(d

B)

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ECC33orig

ECC33modified


50

Table 7 RMSE Values using developed models at 800MHz

Root Mean Square Error Values of Developed Models at 800MHz

Measurement Environment Root Mean Square Error

Adum 11.8494

Techiman 9.6717

Agogo 5.2510

Afrancho 7.1129

New Dorma 29.8491

Table 8 RMSE values using developed models at 2600MHz

Root Mean Square Error Values of Developed Models at 2600MHz

Measurement Environment Root Mean Square Error

Site 1 9.1408

Site 2 13.3313

Site 3 16.8445

Site 4 15.2780

Site 5 11.9498

The improved models developed were further compared with the path loss simulated by the use

of the NYUSIM simulator. This is a simulation tool developed by the New York University

(NYU) wireless team and relies on huge amounts of true measured data at mm-wave frequencies

in New York[27]. The simulator incorporates the CI propagation model [28]. Developed models

show consistent prediction behavior compared with the NYU simulator’s path loss and hence can

be considered valid models for use in the Ghanaian environment with similar environmental

features as the measurement environments.

Figure 14 Comparison of Performance of Developed Models against NYUSIM at 2600MHz

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Distance

110

120

130

140

150

160

170

180

Path

Los

s (dB

)

techiman 1

techiman 2

techiman zongo

sunyani

brekum

Nyusim


51

Figure 15 Comparison of Performance of Developed Models against NYUSIM at 800MHz

5. CONCLUSION

This study was focused on developing improved versions of industry-standard propagation

models suited for LTE path loss prediction in the Ghanaian environment. Path loss of four

propagation models was compared with Path loss of propagation measurements taken from five

LTE 800MHz base stations located in the urban and suburban areas of Ghana using MATLAB.

Results confirmed the initial assumption of the study, that propagation models predict far from

the ideal. The Ericson model showed satisfactory performance in the urban environments at

800MHz. This model however over predicted the path loss in the suburban environments. The

SUI model outperformed the other models in predicting close to the propagation measurement in

suburban areas at 800MHz.The Ericson and SUI models were further improved for a more

accurate prediction of LTE path loss in urban and suburban Ghanaian environments at 800MHz.

For similar studies at an LTE frequency of 2600MHz, the Path loss of five propagation models

was compared with the Path loss of propagation measurements taken at five base stations located

in the urban and suburban areas of Ghana. The ECC-33 model best fit propagation measurements

both in the urban and suburban environments and hence it was developed further for use in LTE

path loss estimation at 2600MHz

From the results presented, measurement data ascertained the fact that propagation models predict

far from the ideal. The modified equations presented in this paper can be used in Ghanaian

settings having similar characteristics with the Areas considered in this paper by network

operators for accurate and simplified path loss prediction.

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50 100 150 200 250 300 350 400 450 500

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